{"title":"利用大规模BSS技术实现大规模MIMO系统的信源恢复","authors":"Zhongqiang Luo, Lidong Zhu, Chengjie Li","doi":"10.1109/ICCCHINA.2014.7008308","DOIUrl":null,"url":null,"abstract":"This paper considers (Blind Source Separation) BSS-based unobserved signal recovery mechanism in large-scale multiple-input multiple-output (MIMO) communication systems, also referred to as “massive MIMO”. On account of the larger number of the observations in massive MIMO system, the separation task will be time-consuming or even failing because the conventional blind separation methods are only suited to small scale system. Therefore, we propose a fast large scale blind separation algorithm to overcome these limitations. Theoretical analysis and simulation results demonstrate that the effective performance of the massive MIMO assisted by the proposed large-scale BSS technique can be acquired.","PeriodicalId":353402,"journal":{"name":"2014 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"43 13","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Exploiting large scale BSS technique for source recovery in massive MIMO systems\",\"authors\":\"Zhongqiang Luo, Lidong Zhu, Chengjie Li\",\"doi\":\"10.1109/ICCCHINA.2014.7008308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers (Blind Source Separation) BSS-based unobserved signal recovery mechanism in large-scale multiple-input multiple-output (MIMO) communication systems, also referred to as “massive MIMO”. On account of the larger number of the observations in massive MIMO system, the separation task will be time-consuming or even failing because the conventional blind separation methods are only suited to small scale system. Therefore, we propose a fast large scale blind separation algorithm to overcome these limitations. Theoretical analysis and simulation results demonstrate that the effective performance of the massive MIMO assisted by the proposed large-scale BSS technique can be acquired.\",\"PeriodicalId\":353402,\"journal\":{\"name\":\"2014 IEEE/CIC International Conference on Communications in China (ICCC)\",\"volume\":\"43 13\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE/CIC International Conference on Communications in China (ICCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCHINA.2014.7008308\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE/CIC International Conference on Communications in China (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCHINA.2014.7008308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploiting large scale BSS technique for source recovery in massive MIMO systems
This paper considers (Blind Source Separation) BSS-based unobserved signal recovery mechanism in large-scale multiple-input multiple-output (MIMO) communication systems, also referred to as “massive MIMO”. On account of the larger number of the observations in massive MIMO system, the separation task will be time-consuming or even failing because the conventional blind separation methods are only suited to small scale system. Therefore, we propose a fast large scale blind separation algorithm to overcome these limitations. Theoretical analysis and simulation results demonstrate that the effective performance of the massive MIMO assisted by the proposed large-scale BSS technique can be acquired.